| commit | b91e35ee30afeb0dfb67f19b68fdada841cdca4c | [log] [tgz] |
|---|---|---|
| author | Nathan Harmata <nharmata@google.com> | Fri Dec 16 02:39:54 2016 +0000 |
| committer | John Cater <jcater@google.com> | Fri Dec 16 15:36:21 2016 +0000 |
| tree | 7a90c2d8245545b3e308abc46b94e33cf1a4394e | |
| parent | 98ed6bcad46adb4934c778af1cac67a5b78df28d [diff] |
Some improvements to ParallelQueryUtils. (i) Use a CountDownLatch in ParallelQueryUtils#executeQueryTasksAndWaitInterruptibly to avoid busy-looping while waiting for query subtask completion (this busy-looping unnecessarily ties up a thread). But we still retain the fail-fast semantics we want (I renamed the method to emphasize this). (ii) Also have a special-case in ParallelQueryUtils#executeQueryTasksAndWaitInterruptibly for evaluating one query subtask so we don't wastefully use another thread. (iii) Also add ThreadSafety annotations to ParallelQueryUtils. ---- (i) and (ii) combine to address the following theoretical issue. Suppose we're evaluating a query expression of the form "(e1 - e2) + (e3 - e4)". The old code would (with the worst-case FJP thread scheduling) have the following threads at the _same_ time: Main QueryCommand thread - executeQueryTasksAndWaitInterruptibly(queryTasks = [(e1 - e2), (e3 - e4)] FJP thread - executeQueryTasksAndWaitInterruptibly(queryTasks = [e2]) FJP thread - eval(e2) FJP thread - executeQueryTasksAndWaitInterruptibly(queryTasks = [e4]) FJP thread - eval(e4) So of those 5 concurrent threads, 3 would be doing busy-loop waiting. For more pathological query expressions, we could end up tying up lots of threads doing wasteful busy-loops. -- PiperOrigin-RevId: 142215680 MOS_MIGRATED_REVID=142215680
{Fast, Correct} - Choose two
Bazel is a build tool that builds code quickly and reliably. It is used to build the majority of Google‘s software, and thus it has been designed to handle build problems present in Google’s development environment, including:
A massive, shared code repository, in which all software is built from source. Bazel has been built for speed, using both caching and parallelism to achieve this. Bazel is critical to Google's ability to continue to scale its software development practices as the company grows.
An emphasis on automated testing and releases. Bazel has been built for correctness and reproducibility, meaning that a build performed on a continuous build machine or in a release pipeline will generate bitwise-identical outputs to those generated on a developer's machine.
Language and platform diversity. Bazel's architecture is general enough to support many different programming languages within Google, and can be used to build both client and server software targeting multiple architectures from the same underlying codebase.
Find more background about Bazel in our FAQ.